Tutorial: Complexity analysis of Singular Value Decomposition and its variants
Numerical Analysis
2019-10-15 v3 Machine Learning
Numerical Analysis
Machine Learning
Abstract
We compared the regular Singular Value Decomposition (SVD), truncated SVD, Krylov method and Randomized PCA, in terms of time and space complexity. It is well-known that Krylov method and Randomized PCA only performs well when k << n, i.e. the number of eigenpair needed is far less than that of matrix size. We compared them for calculating all the eigenpairs. We also discussed the relationship between Principal Component Analysis and SVD.
Cite
@article{arxiv.1906.12085,
title = {Tutorial: Complexity analysis of Singular Value Decomposition and its variants},
author = {Xiaocan Li and Shuo Wang and Yinghao Cai},
journal= {arXiv preprint arXiv:1906.12085},
year = {2019}
}